Originally published as Genetics Published Articles Ahead of Print on February 19, 2006.

Genetics, Vol. 172, 2665-2681, April 2006, Copyright © 2006
doi:10.1534/genetics.105.048975

A Simple and Robust Statistical Test for Detecting the Presence of Recombination

Trevor C. Bruen^*,1, Hervé Philippe and David Bryant^*,

^* McGill Centre for Bioinformatics, McGill University, Montreal, Quebec H3A 2B4, Canada, Program in Evolutionary Biology, Canadian Institute for Advanced Research, Centre Robert Cedergren, Département de Biochimie, Université de Montréal, Montreal, Quebec H3T 1J4, Canada and Department of Mathematics, University of Auckland, Auckland, New Zealand

¹ Corresponding author: McGill Centre for Bioinformatics, Duff Medical Bldg., 3775 University St., Montreal, QC H3A 2B4, Canada.
E-mail: trevor{at}mcb.mcgill.ca

Recombination is a powerful evolutionary force that merges historically distinct genotypes. But the extent of recombination within many organisms is unknown, and even determining its presence within a set of homologous sequences is a difficult question. Here we develop a new statistic, _w, that can be used to test for recombination. We show through simulation that our test can discriminate effectively between the presence and absence of recombination, even in diverse situations such as exponential growth (star-like topologies) and patterns of substitution rate correlation. A number of other tests, Max ², NSS, a coalescent-based likelihood permutation test (from LDHat), and correlation of linkage disequilibrium (both r² and |D'|) with distance, all tend to underestimate the presence of recombination under strong population growth. Moreover, both Max ² and NSS falsely infer the presence of recombination under a simple model of mutation rate correlation. Results on empirical data show that our test can be used to detect recombination between closely as well as distantly related samples, regardless of the suspected rate of recombination. The results suggest that _w is one of the best approaches to distinguish recurrent mutation from recombination in a wide variety of circumstances.

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